Markets Look Beyond the Headline

Introduction

Financial markets react to releases of new information about the state of the economy. One reason for this is that new information about the economy influences a central bank’s upcoming decisions. For example, a release of Canadian inflation data that were higher than expected may suggest to market participants that the Bank of Canada could tighten monetary policy earlier and/or faster than previously expected. A tighter-than-expected monetary policy would in turn increase Canadian bond yields and the value of the Canadian dollar, all else being equal (Feunou et al. 2017).

At the time new information is released, most attention goes to headline news; for example, Canada’s annual inflation was reported at 2.2 per cent for February 2018. However, data releases also contain useful information beyond the headline news. For instance, the February inflation report from Statistics Canada discussed how measures of core inflation had climbed above 2 per cent for the first time since February 2012. This piece of news, which we define as non-headline news, can be significant even though it is not reflected in the headline number. Indeed, we find that headline news in Canada, such as total inflation, real GDP growth and the unemployment rate, can explain only about 30 per cent of the changes in asset prices, within 30 minutes of the release. In contrast, we find that non-headline information can at times explain as much as 60 per cent of asset price changes.

Central banks, including the Bank of Canada, are keenly interested in how financial markets react to new information because this gives them an idea of how well markets understood previous policy announcements. Given that market prices react very little to headline news, to more fully analyze how market participants process new information, it is crucial to also measure how markets react to non-headline news.

Markets react less to headline news than one might expect

Financial markets are forward looking. This means that the expected component in data releases should have virtually no impact on asset prices. Thus, to measure the effects of data releases, we first calculate the surprise component of each piece of headline news. The surprise component is the difference between the macroeconomic data released and the financial market’s expectation. We then estimate the impact of the headline surprise on asset prices using the following linear regression:

\(Δy_{j,t}= α_i\)
\(+\,β_{i,t} Headline_{i,t}\)
\(+\,ε_{i,t}^h,\)

where \(Δy_{j,t} \) denotes the change in asset price \(j\) from minutes before the announcement to 30 minutes after it, and \(t\) indexes announcement days for each news item \(i\). Selected asset prices are the Canadian dollar (CAD), three-month Canadian bankers’ acceptance futures contracts (BAX) and the futures contracts on the 10-year Government of Canada bond (CGB). We select the following news: total inflation, retail sales, real GDP and total employment. Chart 1a and Chart 1b show the average R2s and coefficient estimates from the regressions for each of these news items.

Chart 1a shows that headline surprises explain as little as 10 per cent of asset price changes following news on retail sales and up to only 30 per cent following news on employment numbers. One possible reason why headline surprises explain such a small portion of asset price movements is that other events or announcements could influence prices at the same time. This is highly unlikely, though, since we measure the asset price movements in a narrow time window around the announcement. Instead, we attribute the unexplained change in asset prices to non-headline news.

Markets react strongly to the non-headline news

The surprise component in the non-headline news can be inferred using a filtering technique (Gürkaynak, Kisacikoglu and Wright 2018). The idea behind this technique is simple. Since news announcements affect various asset prices simultaneously, we can assume that the common component of changes in asset prices that is not explained by the headline surprise reflects the non-headline surprise. This technique captures a rich information set contained in the report but not captured by headline news. For instance, by applying this technique to GDP data, we can measure how markets perceived the composition as well as the sources of GDP growth. A key assumption underlying this technique is that this common component does not reflect other external factors, such as US data releases.

We expand the previous regression analysis and find that non-headline news, on average, has a bigger impact on asset prices than does headline news (Chart 1b). As well, we find that non-headline news explains a significant portion of asset price movements. Headline and non-headline news together can explain around 60 per cent of financial asset price movements in the 30 minutes following the release of new data, more than doubling the explanatory power of headline news alone (Chart 1a). Full regression results and computation of Chart 1a and Chart 1b values are provided in the appendix (Table 1 and Table 2).

The relative importance of information beyond the headline changes over time. In our sample, this information sometimes explains most of the asset price changes following the release. To show this, we repeat the exercise using a window with two years of data, and we then roll the window from 2009 to the end of our sample. In the case of GDP data, Chart 2a suggests that headline news was essentially unable to explain any of the asset price changes around 2014. In contrast, Chart 2b suggests that the non-headline component of the report alone could explain around 60 per cent of the asset price changes during the same period. Thus, measuring the response to information beyond headline news is crucial to understanding the market response to new information about the economy.

Conclusion

Asset prices in financial markets react to the headlines of macroeconomic data releases, but they react more strongly to information beyond the headlines. Thus, measuring the extent of this reaction is crucial for understanding the market’s overall reaction to new information about the economy. In this note, we use a relatively simple filtering technique to measure the effect of non-headline surprises. A more sophisticated technique, such as machine learning, could reveal the specific information markets focus on when looking beyond the headlines. We leave this for future work.

Disclaimer

Bank of Canada staff analytical notes are short articles that focus on topical issues relevant to the current economic and financial context, produced independently from the Bank’s Governing Council. This work may support or challenge prevailing policy orthodoxy. Therefore, the views expressed in this note are solely those of the authors and may differ from official Bank of Canada views. No responsibility for them should be attributed to the Bank.